Title
Ontology driven bee's foraging approach based self adaptive online recommendation system
Abstract
Online recommendation system is the modern software system used in all the e-commerce sites to capture the user intent and recommend the web pages that contain user expected information. The important challenges for such a system must include a need of being self-adaptive because the needs for online users may change dynamically. Classifier plays a very important role to improve the overall system accuracy. Here, we proposed the Ontology driven bee's foraging approach (ODBFA) that accurately classify the current user activity to any of the navigation profiles and predict the navigations that most likely to be visited by online users. Our proposed ODBFA method uses the Honey bee foraging behaviour in selecting the more profitable navigation profile for the current user activity. This approach makes the system self adaptive by capturing the changing needs of online user with the help of ontological framework comprising of ontology based similarity comparison and scoring algorithm. This approach effectively outperforms the other methods in achieving accurate classification and prediction of future navigation for the current online user.
Year
DOI
Venue
2012
10.1016/j.jss.2011.12.018
Journal of Systems and Software
Keywords
Field
DocType
self adaptive online recommendation,online user,modern software system,online recommendation system,future navigation,system self adaptive,overall system accuracy,foraging approach,current online user,user intent,current user activity,ontology
Data mining,Ontology,Computer science,Real-time computing,Software system,Artificial intelligence,Classifier (linguistics),Foraging,Recommender system,Scoring algorithm,Self adaptive,User intent,Machine learning
Journal
Volume
Issue
ISSN
85
11
0164-1212
Citations 
PageRank 
References 
3
0.40
19
Authors
5
Name
Order
Citations
PageRank
V. Mohanraj1186.46
M. Chandrasekaran230.40
J. Senthilkumar3216.28
S. Arumugam4162.07
Y. Suresh5214.25